Text feature selection using ant colony optimization

Feature selection and feature extraction are the most important steps in classification systems. Feature selection is commonly used to reduce dimensionality of datasets with tens or hundreds of thousands of features which would be impossible to process further. One of the problems in which feature selection is essential is text categorization. A major problem of text categorization is the high dimensionality of the feature space; therefore, feature selection is the most important step in text categorization. At present there are many methods to deal with text feature selection. To improve the performance of text categorization, we present a novel feature selection algorithm that is based on ant colony optimization. Ant colony optimization algorithm is inspired by observation on real ants in their search for the shortest paths to food sources. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of genetic algorithm, information gain and CHI on the task of feature selection in Reuters-21578 dataset. Simulation results on Reuters-21578 dataset show the superiority of the proposed algorithm.